Pre-trained classifier of electrical motor winding fault based on multiple-branch convolutional neural networks

Pre-trained classifier of electrical motor winding fault based on multiple-branch convolutional neural networks

Software

Software implementation of an artificial neural network for detecting a fault in the winding of an electric motor. The neural network is implemented in the Python environment in the Keras module designed for creating neural networks. The neural network uses the branching capabilities of the neural network. Each input is pre-processed in its own branch using a convolution layer. The results of the individual branches are combined and again evaluated by the resulting Dense layer. The trained network model is able to determine the fault of the electric motor winding.

Software implementation of an artificial neural network for detecting a fault in the winding of an electric motor. The neural network is implemented in the Python environment in the Keras module designed for creating neural networks. The neural network uses the branching capabilities of the neural network. Each input is pre-processed in its own branch using a convolution layer. The results of the individual branches are combined and again evaluated by the resulting Dense layer. The trained network model is able to determine the fault of the electric motor winding.

multiple-branch neural network, convolutional netvork, Python, Keras, electrical motor winding fault

multiple-branch neural network, convolutional netvork, Python, Keras, electrical motor winding fault

Vysoké učení technické v Brně, CEITEC VUT Laboratoř pokročilých senzorů, B1.04 Purkyňova 656/123 612 00 Brno

K využití výsledku jiným subjektem je vždy nutné nabytí licence

https://ai4di.ceitec.cz/vysledky/cnn_fault_classifier